Digital imaging systems are becoming increasingly widespread for producing digital data that can be reconstructed into for example useful radiographic images. In one application of a digital imaging system, radiation from a source is directed toward a subject, typically a patient in a medical diagnostic application, and a portion of the radiation passes through the subject and impacts a detector upon. Upon stimulation by means of light of an appropriate wavelength, the surface of the detector converts the radiation to light photons, which are sensed. In case of computed radiography (CR) the detectors are phosphor screens which can show artifacts such as pits, scratches, cracks, stains, sensitivity variation, dose-response irregularities, etc. In case of one or two dimensional direct radiography (DR) the detector is divided into arrays of independent, solid-state detector elements, and encodes output signals based upon the quantity or intensity of the radiation impacting each detector element. The occurrence of defective response of a single detector element or a set of clustered detector elements or associated driver elements can cause single pixel image artifacts or row- or column-wise line artifacts. The presence of artifacts in the reconstructed images can lead to misinterpretation by the physician. So it is important to regularly check the quality of the detectors. Therefor the detectors are uniformly exposed to X-rays and the reconstructed images are visually inspected for artifact occurrence.
However, visual inspection for artifact occurrence in CR or DR detected images is often time consuming obstructing an efficient workflow. The size of the inspection window matrix on the workstation is often too small to fit the entire detected image. Because of the display-matrix limitations, the even smaller surface-fraction for the inspection window and the vastness of the original image matrix, the original image is sliced into a set of adjacent tile-images prior to artifact inspection at pixel-level. For example a size 35×43 centimeter (cm) CR-detector screen scanned at 100 micron pixel resolution generates a 3.5 K×4.3 K (15 Mega pixel) image. At least twenty different 1K×1K (1 Mpixel) tile-images would be required for inspection to cover all the information present in the original image.
To overcome this problem the original image can be converted into a smaller image fitting into the inspection window using conventional downsizing techniques. In US-A-2003/0218620 a method is described for displaying an electronic document on a digital handheld device whereby first a reduction ratio is calculated equal to a ratio of an original document width to the digital handheld device screen width. Thereafter the reduced document is built having a size related to the original document by the reduction factor. Often used conventional downsizing techniques are sub-sampling or interpolation.
Sub-sampling is a technique that either replicates (copies) or skips row- and column-data in a repetitive way while creating the downsized output image. Since raw image data are passed unattenuated, sub-sampling preserves the amplitude of fine details. However not all possible interesting information is transferred with certainty to the smaller output image. Most of the original image content is inevitably lost by the process-inherent pixel-data skipping. Output image signaling of the finest detail occurrence in the original image is only supported if the pixel replication point coincides with the fine detail location. Guaranteed transfer of all information relevant for inspection between the original image and the resulting downsized output image is by consequence impossible.
Interpolation is an alternative technique often used for image resizing. Known interpolation techniques are the smoothing average, nearest neighbour, bi-linear, bi-cubic spline and high-resolution cubic spline kernels. Interpolation is a technique that uses spatial convolution of the pixel-data with the interpolation kernel coefficients to obtain a downsized image. Due to this the medium and high spatial frequency components, mainly representing fine image disturbances, are represented with higher attenuation than the lower spatial frequency components after resizing. The attenuation-level depends on the frequency-response curve, mathematically linked to the spatial scope and shape of the convolution kernel-coefficients used. As these kernel-coefficients are different (except for the smoothing average kernel), the attenuation-level also depends on the location of the image-disturbance relative to the interpolation point. Both effects lead to reduced detectability of isolated, medium and high spatial frequency disturbances, present in the original image, after downsizing.
To overcome the above mentioned problems a need exists for an improved workflow to detect artifacts during visual image inspection whereby the original image is converted into an image fitting the inspection window and image overview is preserved. Furthermore there is a need to signal the presence of artifacts with certainty, regardless of their spatial frequency content or image location, such that they can be detected more easily during visual artifact inspection.